Article in Actitis, Issue 48Beak presents results of monitoring of breeding, resting and migratory birds at a wind power site in the post-mining landscape near Hoyerswerda

11th October 2017, Freiberg (Germany)

In 2004/05, the company Beak Consultants GmbH supported the application for the planning permission of the wind farm Elsterheide in the post-mining landscape near Hoyerswerda. Beak was contracted with the compilation of environmental statements, mitigation and compensation plans and bird & bat survey and monitoring. Between autumn 2006 and the end of 2011, a monitoring of breeding and migratory birds was conducted in collaboration with the Nature Conservation Institute of Dresden and the Nature Conservation Institute of Freiberg. In January 2017, the results were published in the journal Actitis (NABU Sachsen e.V.), issue 48, which is available here. The study showed that the development of the wind farm in this post-mining landscape which is valuable for wild birds did not lead to a decline in species’ numbers or range after five years – including endangered species such as Hoopoe, Nightjar, Grey Shrike, Woodlark or Tawny Pipit.

Already in January 2016, an article about the analysis of the relationship between habitat/land-use data and bird territories with the help of geo-information systems and artificial neural networks was published in the previous Actitis, issue 47. In this article, the results of a master’s thesis from the University of Applied Science of Dresden (Hochschule für Technik und Wirtschaft, HTW), Faculty of Geoinformation, were presented, which have been conducted with the help of Beak’s advangeo® Prediction Software and the technical support by Beak staff. The presented examples show a general suitability of artificial neural networks (ANN), as they are implemented in advangeo®, to predict the spatial distribution as well as the frequency of breeding bird species. The main requirement for the training of the ANN are appropriate background data. For most of the investigated species, a good prediction of their abundancy and frequency was achieved. The obvious reasons for less good learning results of the ANN for some species are discussed as well as suggestions are being made for working with the available geodata and methods. The issue can be purchased here.